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Reliability Basics | ||

Characterizing Your
Product's Reliability
As awareness of product reliability increases, so does the responsibility of engineering organizations to insure that reliability requirements are met. As a result, engineers and managers who have had little experience with life data analysis or applied statistics may find themselves responsible for calculating and reporting on a product's reliability. This article offers background on elementary concepts of reliability and life data analysis that will hopefully prove to be of use to the novice analyst. We will begin our discussion with an overview of how to characterize your product's reliability. The most general purpose
of life data analysis is to characterize the life behavior of a product. We
will assume that the product in question is non-repairable ( In more simple terms, the purpose of reliability analysis is to indicate the probability of success for a specified time. This probability is called the reliability and is always associated with a given time. That is, the given percentage representing the probability of success is a function of time and is essentially paired with an associated time. For example, a specification may call for a 90% reliability at 100 hours of operation. This means that the product has a 90% probability of running for 100 hours without failure. It can also be interpreted as 90% of a population of such products will run for 100 hours, while the other 10% will have failed before 100 hours. Other reliability/time combinations will hold true for the same product. For example, the products in the previous example may have a reliability of 75% at 200 hours. The relationship between reliability and operation time for a product can generally be characterized by a continuous reliability function or curve, which represents reliability as a function of time. This function is usually denoted as R(t), with R representing the dependent variable reliability and t representing the independent variable time. A graphical representation of such a function is shown in the following figure: This represents the probability of failure over the lifetime of the product and is one of the fundamental measures in life data analysis. One other reliability
metric that merits quick discussion is that of the mean life, or MTBF/MTTF.
This is widely used as a reliability metric due to its simplicity. However,
it is very easy to become overly reliant on this metric, which is often
thought to be synonymous with a reliability of 50%. However, this is not
always the case and the use of the MTBF in these circumstances may result in
misleading characterizations of a products reliability. For a detailed
discussion on the unsuitability of the MTBF as the sole reliability metric,
see the "The
Limitations of Using the MTTF as a Reliability Specification" article in
this issue of Failure data may be
obtained from a reliability or life test conducted in a controlled
environment, the purpose of which is to operate units to failure in order to
obtain data for reliability analysis. Ideally, all of the units put on the
test should be operated until they fail, resulting in a data set comprised
of complete data. Sometimes this is not possible due to time and budgetary
constraints and there will be accumulated test time for units that did not
fail. This is known as suspended data and, while not as important as
complete failure data, it should not be discarded. This is because the
information it contains ( | ||

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